
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
							from ...tokenization_utils_base import TextInput
from ...utils import logging


A__							: int     =       logging.get_logger(__name__)

A__							: str     =       {'vocab_file': 'spiece.model'}

A__							: List[Any]     =       {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A__							: Tuple     =       {
    't5-small': 5_1_2,
    't5-base': 5_1_2,
    't5-large': 5_1_2,
    't5-3b': 5_1_2,
    't5-11b': 5_1_2,
}

A__							: List[Any]     =       '▁'





class     lowercase							(	_A       ):
					__a											=     VOCAB_FILES_NAMES
					__a											=     PRETRAINED_VOCAB_FILES_MAP
					__a											=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a											=     ["""input_ids""", """attention_mask"""]
					def __init__(		self			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__="</s>"			,						SCREAMING_SNAKE_CASE__="<unk>"			,						SCREAMING_SNAKE_CASE__="<pad>"			,						SCREAMING_SNAKE_CASE__=100			,						SCREAMING_SNAKE_CASE__=None			,						SCREAMING_SNAKE_CASE__ = None			,						SCREAMING_SNAKE_CASE__=True			,						**SCREAMING_SNAKE_CASE__			,						):
								"""simple docstring"""







								if extra_ids > 0 and additional_special_tokens is None:
											lowerCAmelCase__			:     int					   =      [f'''<extra_id_{i}>''' for i in range(UpperCamelCase__     )]
								elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra_id special tokens
											lowerCAmelCase__			:     Dict					   =      len(set(filter(lambda SCREAMING_SNAKE_CASE__     : bool('''extra_id''' in str(UpperCamelCase__     )     )			,						UpperCamelCase__     )     )     )
											if extra_tokens != extra_ids:
														raise ValueError(
														    f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
														    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
														    ''' tokens'''     )
								if legacy:
											logger.warning_once(
											    f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
											    ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''     )

								lowerCAmelCase__			:     int					   =      legacy
								lowerCAmelCase__			:     Optional[Any]					   =      {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=UpperCamelCase__			,						unk_token=UpperCamelCase__			,						pad_token=UpperCamelCase__			,						extra_ids=UpperCamelCase__			,						additional_special_tokens=UpperCamelCase__			,						sp_model_kwargs=self.sp_model_kwargs			,						legacy=UpperCamelCase__			,						**UpperCamelCase__			,						)

								lowerCAmelCase__			:     List[str]					   =      vocab_file
								lowerCAmelCase__			:     Tuple					   =      extra_ids

								lowerCAmelCase__			:     Union[str, Any]					   =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(UpperCamelCase__     )
					@staticmethod
					def lowercase_      (		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
											lowerCAmelCase__			:     List[Any]					   =      TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
											if init_max_model_length is not None and init_max_model_length != max_model_length:
														return init_max_model_length
											elif init_max_model_length is None:
														warnings.warn(
														    '''This tokenizer was incorrectly instantiated with a model max length of'''
														    f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
														    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
														    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
														    f''' {pretrained_model_name_or_path} automatically truncating your input to'''
														    f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
														    f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
														    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
														    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''			,						UpperCamelCase__			,						)

								return max_model_length
					@property
					def lowercase_      (		self     ):
								"""simple docstring"""







								return self.sp_model.get_piece_size() + self._extra_ids
					def lowercase_      (		self     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Any					   =      {self.convert_ids_to_tokens(UpperCamelCase__     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__ = None			,						SCREAMING_SNAKE_CASE__ = False     ):
								"""simple docstring"""







								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=UpperCamelCase__			,						token_ids_a=UpperCamelCase__			,						already_has_special_tokens=UpperCamelCase__     )

								# normal case: some special tokens
								if token_ids_a is None:
											return ([0] * len(UpperCamelCase__     )) + [1]
								return ([0] * len(UpperCamelCase__     )) + [1] + ([0] * len(UpperCamelCase__     )) + [1]
					def lowercase_      (		self     ):
								"""simple docstring"""







								return list(
								    set(filter(lambda SCREAMING_SNAKE_CASE__     : bool(re.search(R'''<extra_id_\d+>'''			,						UpperCamelCase__     )     ) is not None			,						self.additional_special_tokens     )     )     )
					def lowercase_      (		self     ):
								"""simple docstring"""







								return [self._convert_token_to_id(UpperCamelCase__     ) for token in self.get_sentinel_tokens()]
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								if len(UpperCamelCase__     ) > 0 and token_ids[-1] == self.eos_token_id:
											warnings.warn(
											    f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
											    ''' eos tokens being added.'''     )
											return token_ids
								else:
											return token_ids + [self.eos_token_id]
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__ = None     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Union[str, Any]					   =      [self.eos_token_id]

								if token_ids_a is None:
											return len(token_ids_a + eos     ) * [0]
								return len(token_ids_a + eos + token_ids_a + eos     ) * [0]
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__ = None     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Optional[int]					   =      self._add_eos_if_not_present(UpperCamelCase__     )
								if token_ids_a is None:
											return token_ids_a
								else:
											lowerCAmelCase__			:     int					   =      self._add_eos_if_not_present(UpperCamelCase__     )
											return token_ids_a + token_ids_a
					def __getstate__(		self     ):
								"""simple docstring"""







								lowerCAmelCase__			:     List[str]					   =      self.__dict__.copy()
								lowerCAmelCase__			:     Optional[Any]					   =      None
								return state
					def __setstate__(		self			,						SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Union[str, Any]					   =      d

								# for backward compatibility
								if not hasattr(self			,						'''sp_model_kwargs'''     ):
											lowerCAmelCase__			:     Union[str, Any]					   =      {}

								lowerCAmelCase__			:     Optional[int]					   =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(self.vocab_file     )
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__			,						**SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								if not self.legacy:
											lowerCAmelCase__			:     Union[str, Any]					   =      SPIECE_UNDERLINE + text.replace(UpperCamelCase__			,						''' '''     )
								return super().tokenize(UpperCamelCase__			,						**UpperCamelCase__     )
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__			,						**SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								if not self.legacy:
											lowerCAmelCase__			:     List[Any]					   =      text.startswith(UpperCamelCase__     )
											if is_first:
														lowerCAmelCase__			:     Optional[int]					   =      text[1:]

								lowerCAmelCase__			:     List[Any]					   =      self.sp_model.encode(UpperCamelCase__			,						out_type=UpperCamelCase__     )

								if not self.legacy and not is_first and not text.startswith(''' '''     ) and tokens[0].startswith(UpperCamelCase__     ):
											lowerCAmelCase__			:     List[str]					   =      ([tokens[0][1:]] if len(tokens[0]     ) > 1 else []) + tokens[1:]
								return tokens
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								if token.startswith('''<extra_id_'''     ):
											lowerCAmelCase__			:     Dict					   =      re.match(R'''<extra_id_(\d+)>'''			,						UpperCamelCase__     )
											lowerCAmelCase__			:     List[Any]					   =      int(match.group(1     )     )
											return self.vocab_size - num - 1
								return self.sp_model.piece_to_id(UpperCamelCase__     )
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								if index < self.sp_model.get_piece_size():
											lowerCAmelCase__			:     List[str]					   =      self.sp_model.IdToPiece(UpperCamelCase__     )
								else:
											lowerCAmelCase__			:     Dict					   =      f'''<extra_id_{self.vocab_size - 1 - index}>'''
								return token
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Dict					   =      []
								lowerCAmelCase__			:     Any					   =      ''''''
								lowerCAmelCase__			:     str					   =      False
								for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
														if not prev_is_special:
																	out_string += " "
														out_string += self.sp_model.decode(UpperCamelCase__     ) + token
														lowerCAmelCase__			:     int					   =      True
														lowerCAmelCase__			:     List[str]					   =      []
											else:
														current_sub_tokens.append(UpperCamelCase__     )
														lowerCAmelCase__			:     List[Any]					   =      False
								out_string += self.sp_model.decode(UpperCamelCase__     )
								return out_string.strip()
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__ = None     ):
								"""simple docstring"""







								if not os.path.isdir(UpperCamelCase__     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								lowerCAmelCase__			:     Union[str, Any]					   =      os.path.join(
								    UpperCamelCase__			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

								if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCamelCase__     ) and os.path.isfile(self.vocab_file     ):
											copyfile(self.vocab_file			,						UpperCamelCase__     )
								elif not os.path.isfile(self.vocab_file     ):
											with open(UpperCamelCase__			,						'''wb'''     ) as fi:
														lowerCAmelCase__			:     List[str]					   =      self.sp_model.serialized_model_proto()
														fi.write(UpperCamelCase__     )

								return (out_vocab_file,)





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase     :	Tuple       			=  {'configuration_timm_backbone': ['TimmBackboneConfig']}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	str       			=  ['TimmBackbone']


if TYPE_CHECKING:
    from .configuration_timm_backbone import TimmBackboneConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timm_backbone import TimmBackbone

else:
    import sys

    __lowerCAmelCase     :	str       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

